Feature Extraction Optimization in Neural Classifier of Heart Rate Variability Signals

In this paper neural classifier system preliminary feature extraction and selection process using time-frequency representation of heart rate variability (HRV) signal is presented. The crucial point of described method is hybrid multi-domain feature set creation, combining different type parameters as well as feature selection based on the measure of class separability property, computed for each extracted feature. Regarding specific properties of non-stationary HRV signal, wavelet transform was chosen as time-frequency representation tool. Presented results are connected both with optimal feature extraction and selection of HRV signals from patient with coronary artery disease as well as classifier performance verification.